The present application relates generally to processing of signals such as radio frequency signals, and more particularly to a device and method of separating additive signal sources and detecting signal noise, wanted or unwanted signals.
When taking signal measurements in uncontrolled settings, the signal of interest is usually corrupted with unwanted noise or interference from an external unwanted source and/or from an internal source of noise caused by the measuring instrument itself. In general, the noise-component is assumed to be additive to the signal component, resulting in a measurement of mixed signals. However, it becomes challenging when the goal is to study only one component, for example, the underlying signal of interest. For example, in radio-frequency transmissions there are many possible sources of noise that are present, and a cooperative transmitter will need to accurately differentiate the signal of interest from all the noises. One way is to raise the transmission power significantly above the noise floor, but such mechanism is costly and there may be prohibiting rules preventing such mechanism.
Various methods exist for separating signals and noise. Radio frequency interference (RFI), for example, is a central topic where the goal revolves around detection and removal of noise from wanted signal. In radio astronomy signal analysis, the sum-threshold method is used on spectrograms to mask noise, and morphological operations may be used to enhance the masking result.
In the field of radio astronomy, for instance, radio telescopes are very sensitive to RFI which, if picked up, severely affects the astronomical signal of interest. While deep learning techniques like Convolutional Neural Networks (CNNs) and Long Short-term Memories (LSTMs) can be applied for the purpose of classifying sources of transient RFI, in order to successfully classify RFI in a real-world setting, accurate RFI detection first needs to be performed.